The review of the most used computational methods for studies of the relationships between molecular structure and biological activity
DOI:
https://doi.org/10.15587/2313-8416.2015.51463Keywords:
QSAR, drug development, molecular modeling, QSAR methods, mathematical models, variable selection methods, machine learningAbstract
Aim. To systematize the most used methods of “molecular structure-biological activity” relationship studies and to disclose their principles of application, strong and weak sides.
Methods. The review of modern scientific literature devoted to QSAR modeling was carried out. The most frequently used methods for “structure-activity” models development were selected for further description.
Results. The place of “molecular structure-activity” relationships analysis among computer assisted drug design methods is discussed in the current review and the most used algorithms of QSAR model development with emphasis on the mechanisms of their work are described. The approaches based on model ensembles become more and more popular, one of which is Random Forest.
Conclusions. The progress in machine learning methods development is the key to the further evolution of QSAR direction and to the discovering of new biologically active substances
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